A new rough set model based on multi-scale covering

Li, Weikang; Li, Jinjin; Huang, Jianxin; Dai, Weizhong; Zhang, Xiaoping

doi:10.1007/s13042-020-01169-5

Cited by 16 publications

(3 citation statements)

References 31 publications

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“…Axiomatic characterizations of covering-based approximation operators guarantee the existence of coverings reproducing the operators. In terms of multi-scale covering, Li [ 7 ] constructed the scale relation based on covering and extended the multi-scale covering rough set model. Since then, the relation between scales is no longer limited to the usual coarse and fine relationships.…”

Section: Related Workmentioning

confidence: 99%

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A new mechanism of rule acquisition based on covering rough sets

Zhang

2022

Appl Intell

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Rule acquisition, known as knowledge acquisition, is an important and topical issue in granular computing theory. Granules are not only composed of objects but also have feature values. However, In the granule associativity rules, the traditional rule extraction methods fail to consider the influence of granules on the decision, thus the method is not well adapted in reality. On the other hand, the existing methods lack a rule-based measure for information systems. In this paper, the action parameters are first introduced to establish a more realistic granule associativity rule in covering information system. Further, we present the rule-based data potential to address the measurement problem. In addition, rule-based scale selection in multi-scale covering rough sets is explored, followed by a scale combination integrating generalization capability, data potential, and lower approximation. Finally, algorithm is designed and experiments are conducted.

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Section: Related Workmentioning

confidence: 99%

“…However, these studies are limited to single-scale covering. Subsequently, some scholars extended the covering rough set from single scale to multi-scale [ 7 – 10 , 25 ]. It is more comprehensive and flexible to apply multi-scale covering rough set to data mining.…”

Section: Introductionmentioning

confidence: 99%

A new mechanism of rule acquisition based on covering rough sets

Zhang

2022

Appl Intell

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“…Owing to different scales of attribute value will contribute to multigranulation, Wu and Leung [10] put forward multi-scale information system for the first time, which was further improved and popularized by Li and Hu [11]. And Li et al [12] proposed a multi-scale covering decision information system which is used to select the optimal scale combination of incomplete multi-scale information system. Not surprisingly, the multi-scale data processing methods have been continually concerned by researchers, and various improved methods and models have been proposed [13]- [18].…”

Section: Introductionmentioning

confidence: 99%

Information Entropy and Optimal Scale Combination in Multi-Scale Covering Decision Systems

Chen

Lin

et al. 2020

IEEE Access

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In the traditional covering information systems, each object has only one scale, that is, the single-scale covering information system. But in realistic applications, one often needs to process and analyze data in different scales. To solve this problem, a new multi-scale covering information system is first introduced in this paper, and the optimal scale combination selections based on information entropy in multi-scale coveing decision systems are discussed. Firstly, entropy consistent scale combination and entropy optimal scale combination are defined in multi-scale covering information system. Secondly, entropy optimal scale combination is defined in multi-scale covering decision system, which is proved to be equivalent to the optimal scale combination in consistent multi-scale coveing decision system. Moreover, lower approximation entropy optimal scale combination is defined in the inconsistent multi-scale covering decision system. Finally, the corresponding selection algorithms and specific examples are given by using conditional entropy and limitary conditional entropy, respectively. INDEX TERMS Covering information system, information entropy, multi-scale covering decision system, optimal scale combination.

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